code: def forward(self, audio, video):
      audio, video = self.preprocessor(audio, video)
      feats = self.model(audio.unsqueeze(0), video.unsqueeze(0))
      length = torch.tensor([feats.size(1)], device=audio.device)
      self.hypotheses, self.state = self.decoder.infer(
          feats,
          length,
          10,
          state=self.state,
          hypothesis=self.hypotheses,
      )
      transcript = self.token_processor(
          self.hypotheses[0][0], lstrip=False
      )
      return transcript
  
imports: import numpy
  import sentencepiece
  import torch
  import torchaudio
  import torchvision
  import sys
  from avsr.data_prep.detectors.mediapipe.detector import LandmarksDetector
  from avsr.data_prep.detectors.mediapipe.video_process import VideoProcess
  from torchaudio.utils import download_asset
label_desc: Use image node_id 2 for calling the InferencePipeline obj's underlying member method code with example usage: obj.forward(audio, video) and returns: transcript